Source localization in magnetoencephalography using an iterative weighted minimum norm algorithm

Imaging of brain activity based on magnetoencephalography (MEG) requires high-resolution estimates that closely approximate the spatial distribution of the underlying currents. The authors examine the physics of the MEG problem to give the motivation for developing a new algorithm that meets its unique requirements. The technique is a nonparametric, iterative, weighted norm minimization procedure with a posteriori constraints. The authors develop the algorithm and determine the necessary requirements for convergence. Issues of initialization and bias equalization for MEG reconstruction, and techniques for analysis of noisy data are discussed